Information reporting for anomaly detection

ABSTRACT

In one embodiment, a first device in a network receives traffic flow data from a plurality of devices in the network. The traffic flow data from at least one of the plurality of devices comprises raw packets of a traffic flow. The first device selects a set of reporting devices from among the plurality of devices based on the received traffic flow data. The first device provides traffic flow reporting instructions to the selected set of reporting devices. The traffic flow reporting instructions cause each reporting device to provide sampled traffic flow data to an anomaly detection device.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to information reporting techniques for anomaly detectionin a computer network.

BACKGROUND

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests, to prevent legitimaterequests from being processed. A DoS attack may also be distributed, toconceal the presence of the attack. For example, a distributed DoS(DDoS) attack may involve multiple attackers sending malicious requests,making it more difficult to distinguish when an attack is underway. Whenviewed in isolation, a particular one of such a request may not appearto be malicious. However, in the aggregate, the requests may overload aresource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example self learning network (SLN)infrastructure;

FIGS. 4A-4B illustrate an example of network devices reporting theirtraffic flow monitoring capabilities;

FIGS. 5A-5B illustrate examples of network devices providing trafficflow data;

FIGS. 6A-6E illustrate examples of a flow reporting strategy beingconfigured in a network;

FIG. 7 illustrates an example simplified procedure for implementing atraffic flow reporting strategy; and

FIG. 8 illustrates an example simplified procedure for receiving atraffic flow reporting instruction.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a first devicein a network receives traffic flow data from a plurality of devices inthe network. The traffic flow data from at least one of the plurality ofdevices comprises raw packets of a traffic flow. The first deviceselects a set of reporting devices from among the plurality of devicesbased on the received traffic flow data. The first device providestraffic flow reporting instructions to the selected set of reportingdevices. The traffic flow reporting instructions cause each reportingdevice to provide sampled traffic flow data to an anomaly detectiondevice.

In further embodiments, a first device in a network provides capabilitydata to a second device in the network. The capability data isindicative of whether the first device is operable to generatesummarized traffic flow records. The first device receives a request fortraffic flow data from the second device based on the capability data.The first device provides the requested traffic flow data to the seconddevice, in response to receiving the request for the traffic flow data.The first device receives a traffic flow reporting instruction from thesecond device based on the traffic flow data provided to the seconddevice. The traffic flow reporting instruction causes the first deviceto report sampled traffic flow data to an anomaly detection device.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement (SLA) characteristics. For the sakeof illustration, a given customer site may fall under any of thefollowing categories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potential a backup link (e.g., a3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed SLA, whereasInternet links may either have no SLA at all or a loose SLA (e.g., a“Gold Package” Internet service connection that guarantees a certainlevel of performance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local networks 160, 162 that include devices/nodes 10-16and devices/nodes 18-20, respectively, as well as a data center/cloudenvironment 150 that includes servers 152-154. Notably, local networks160-162 and data center/cloud environment 150 may be located indifferent geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, network 100 may include one or more mesh networks,such as an Internet of Things network. Loosely, the term “Internet ofThings” or “IoT” refers to uniquely identifiable objects (things) andtheir virtual representations in a network-based architecture. Inparticular, the next frontier in the evolution of the Internet is theability to connect more than just computers and communications devices,but rather the ability to connect “objects” in general, such as lights,appliances, vehicles, heating, ventilating, and air-conditioning (HVAC),windows and window shades and blinds, doors, locks, etc. The “Internetof Things” thus generally refers to the interconnection of objects(e.g., smart objects), such as sensors and actuators, over a computernetwork (e.g., via IP), which may be the public Internet or a privatenetwork.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., s are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise routing process244 (e.g., routing services) and illustratively, a self learning network(SLN) process 248 and/or a flow reporting process 249, as describedherein, any of which may alternatively be located within individualnetwork interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Routing process/services 244 include computer executable instructionsexecuted by processor 220 to perform functions provided by one or morerouting protocols, such as the Interior Gateway Protocol (IGP) (e.g.,Open Shortest Path First, “OSPF,” andIntermediate-System-to-Intermediate-System, “IS-IS”), the Border GatewayProtocol (BGP), etc., as will be understood by those skilled in the art.These functions may be configured to manage a forwarding informationdatabase including, e.g., data used to make forwarding decisions. Inparticular, changes in the network topology may be communicated amongrouters 200 using routing protocols, such as the conventional OSPF andIS-IS link-state protocols (e.g., to “converge” to an identical view ofthe network topology).

Notably, routing process 244 may also perform functions related tovirtual routing protocols, such as maintaining VRF instance, ortunneling protocols, such as for MPLS, generalized MPLS (GMPLS), etc.,each as will be understood by those skilled in the art. Also, EVPN,e.g., as described in the IETF Internet Draft entitled “BGP MPLS BasedEthernet VPN” <draft-ietf-12vpn-evpn>, introduce a solution formultipoint L2VPN services, with advanced multi-homing capabilities,using BGP for distributing customer/client media access control (MAC)address reach-ability information over the core MPLS/IP network.

SLN process 248 includes computer executable instructions that, whenexecuted by processor(s) 220, cause device 200 to perform anomalydetection functions as part of an anomaly detection infrastructurewithin the network. In general, anomaly detection attempts to identifypatterns that do not conform to an expected behavior. For example, inone embodiment, the anomaly detection infrastructure of the network maybe operable to detect network attacks (e.g., DDoS attacks, the use ofmalware such as viruses, rootkits, etc.). However, anomaly detection inthe context of computer networking typically presents a number ofchallenges: 1.) a lack of a ground truth (e.g., examples of normal vs.abnormal network behavior), 2.) being able to define a “normal” regionin a highly dimensional space can be challenging, 3.) the dynamic natureof the problem due to changing network behaviors/anomalies, 4.)malicious behaviors such as malware, viruses, rootkits, etc. may adaptin order to appear “normal,” and 5.) differentiating between noise andrelevant anomalies is not necessarily possible from a statisticalstandpoint, but typically also requires domain knowledge.

Anomalies may also take a number of forms in a computer network: 1.)point anomalies (e.g., a specific data point is abnormal compared toother data points), 2.) contextual anomalies (e.g., a data point isabnormal in a specific context but not when taken individually), or 3.)collective anomalies (e.g., a collection of data points is abnormal withregards to an entire set of data points).

In various embodiments, SLN process 248 may utilize machine learningtechniques, to perform anomaly detection in the network. In general,machine learning is concerned with the design and the development oftechniques that take as input empirical data (such as network statisticsand performance indicators), and recognize complex patterns in thesedata. One very common pattern among machine learning techniques is theuse of an underlying model M, whose parameters are optimized forminimizing the cost function associated to M, given the input data. Forinstance, in the context of classification, the model M may be astraight line that separates the data into two classes (e.g., labels)such that M=a*x+b*y+c and the cost function would be the number ofmisclassified points. The learning process then operates by adjustingthe parameters a,b,c such that the number of misclassified points isminimal. After this optimization phase (or learning phase), the model Mcan be used very easily to classify new data points. Often, M is astatistical model, and the cost function is inversely proportional tothe likelihood of M, given the input data.

Computational entities that rely on one or more machine learningtechniques to perform a task for which they have not been explicitlyprogrammed to perform are typically referred to as learning machines. Inparticular, learning machines are capable of adjusting their behavior totheir environment. For example, a learning machine may dynamically makefuture predictions based on current or prior network measurements, maymake control decisions based on the effects of prior control commands,etc.

For purposes of anomaly detection in a network, a learning machine mayconstruct a model of normal network behavior, to detect data points thatdeviate from this model. For example, a given model (e.g., a supervised,un-supervised, or semi-supervised model) may be used to generate andreport anomaly scores to another device. Example machine learningtechniques that may be used to construct and analyze such a model mayinclude, but are not limited to, nearest neighbor (NN) techniques (e.g.,k-NN models, replicator NN models, etc.), statistical techniques (e.g.,Bayesian networks, etc.), clustering techniques (e.g., k-means, etc.),neural networks (e.g., reservoir networks, artificial neural networks,etc.), support vector machines (SVMs), or the like.

One class of machine learning techniques that is of particular use inthe context of anomaly detection is clustering. Generally speaking,clustering is a family of techniques that seek to group data accordingto some typically predefined notion of similarity. For instance,clustering is a very popular technique used in recommender systems forgrouping objects that are similar in terms of people's taste (e.g.,because you watched X, you may be interested in Y, etc.). Typicalclustering algorithms are k-means, density based spatial clustering ofapplications with noise (DBSCAN) and mean-shift, where a distance to acluster is computed with the hope of reflecting a degree of anomaly(e.g., using a Euclidian distance and a cluster based local outlierfactor that takes into account the cluster density).

Replicator techniques may also be used for purposes of anomalydetection. Such techniques generally attempt to replicate an input in anunsupervised manner by projecting the data into a smaller space (e.g.,compressing the space, thus performing some dimensionality reduction)and then reconstructing the original input, with the objective ofkeeping the “normal” pattern in the low dimensional space. Exampletechniques that fall into this category include principal componentanalysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP)ANNs (e.g., for non-linear models), and replicating reservoir networks(e.g., for non-linear models, typically for time series).

An example self learning network (SLN) infrastructure that may be usedto detect network anomalies is shown in FIG. 3, according to variousembodiments. Generally, network devices may be configured to operate aspart of an SLN infrastructure to detect, analyze, and/or mitigatenetwork anomalies such as network attacks (e.g., by executing SLNprocess 248 and/or flow reporting 249). Such an infrastructure mayinclude certain network devices acting as distributed learning agents(DLAs) and one or more supervisory/centralized devices acting as asupervisory learning agent (SLA). A DLA may be operable to monitornetwork conditions (e.g., router states, traffic flows, etc.), performanomaly detection on the monitored data using one or more machinelearning models, report detected anomalies to the SLA, and/or performlocal mitigation actions. Similarly, an SLA may be operable tocoordinate the deployment and configuration of the DLAs (e.g., bydownloading software upgrades to a DLA, etc.), receive information fromthe DLAs (e.g., detected anomalies/attacks, compressed data forvisualization, etc.), provide information regarding a detected anomalyto a user interface (e.g., by providing a webpage to a display, etc.),and/or analyze data regarding a detected anomaly using more CPUintensive machine learning processes.

As shown in FIG. 3, routers CE-2 and CE-3 may be configured as DLAs andserver 152 may be configured as an SLA, in one implementation. In such acase, routers CE-2 and CE-3 may monitor traffic flows, router states(e.g., queues, routing tables, etc.), or any other conditions that maybe indicative of an anomaly in network 100. As would be appreciated, anynumber of different types of network devices may be configured as a DLA(e.g., routers, switches, servers, blades, etc.) or as an SLA.

Assume, for purposes of illustration, that CE-2 acts as a DLA thatmonitors traffic flows associated with the devices of local network 160(e.g., by comparing the monitored conditions to one or moremachine-learning models). For example, assume that device/node 10 sendsa particular traffic flow 302 to server 154 (e.g., an applicationserver, etc.). In such a case, router CE-2 may monitor the packets oftraffic flow 302 and, based on its local anomaly detection mechanism,determine that traffic flow 302 is anomalous. Anomalous traffic flowsmay be incoming, outgoing, or internal to a local network serviced by aDLA, in various cases.

As noted above, anomaly and attack detection may be performed bycapturing information about traffic flows in the network. However, a DLAused to detect anomalies may not be able to analyze traffic flows thatdo not traverse the device. In addition, although a DLA may typicallyemploy a lightweight anomaly detection process (e.g., a lightweightversion of SLN process 248), not every networking devices may be capableof hosting such a process. This may be the case, for example, innon-modular platforms (e.g., platforms that do not support Linuxcontainers, etc.). Even if all of the networking devices are capable ofacting as a DLA, it may still be desirable to select only a subset ofdevices to do so, as using too many DLAs may lead to an overlydistributed (e.g., fragmented) view of the network traffic flows. Thus,some SLN implementations may employ only a limited number of DLAs persite through which only a subset of traffic flows traverse.

Information Reporting for Anomaly Detection

The techniques herein provide mechanisms that allow a DLA to receivetraffic flow data regarding flows that do not traverse the DLA (e.g.,certain intra-site flows, etc.). In one aspect, the DLA may learn thecapabilities of each networking device in the local network with respectto flow monitoring. Notably, only certain network elements may beconfigured to capture and summarize information regarding traffic flows.For network devices that are not configured to capture traffic flowinformation, mechanisms are also disclosed herein whereby such a devicemay forward copies of the flow packets traversing the device to the DLAfor purposes of anomaly detection. In further aspects, techniques hereinare disclosed to select an optimal set of flow reporting devices (e.g.,a minimal set of reporting devices, etc.). Scheduling techniques arealso employed to strictly control the reporting of flow-basedinformation to the DLA, so as not to cause congestion in the network(e.g., by imposing traffic shaping on the reported information, byrecoloring traffic, by instructing a reporting device to use a differentrouting path, etc.).

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a first device in a network receives trafficflow data from a plurality of devices in the network. The traffic flowdata from at least one of the plurality of devices comprises raw packetsof a traffic flow. The first device selects a set of reporting devicesfrom among the plurality of devices based on the received traffic flowdata. The first device provides traffic flow reporting instructions tothe selected set of reporting devices. The traffic flow reportinginstructions cause each reporting device to provide sampled traffic flowdata to an anomaly detection device.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with the flowreporting process 249, which may include computer executableinstructions executed by the processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein, e.g., in conjunction with routing process 244 (and/orSLN process 248).

Operationally, certain network devices may be configured to distinguishtraffic flows traversing the devices. In general, a networking devicemay identify a traffic flow based on any or all of the following: thesource address of the packets of the flow, the source port of thepackets, the destination address of the packets, the destination port ofthe packets, a Differentiated Services Code Point (DSCP) value of thepackets or similar QoS information, an ingress interface identifier, orthe like. In this way, a device may uniquely identify the differenttraffic flows flowing through the device by examining the packets in theflows (e.g., by creating a flow identifier using this information). Forexample, networking devices using NetFlow by Cisco Systems, Inc. or asimilar technology may be operable to distinguish between differenttraffic flows.

A device configured to distinguish between traffic flows may also beoperable to monitor and summarize various information regarding anidentified traffic flow. For example, a networking device may captureand summarize flow duration metrics for a given flow, size metrics forthe flow (e.g., the number of packets in the flow, the number ofobserved bytes of the packets, etc.), Internet Control Message Protocol(ICMP) information, or any other observable information that may beassociated with a flow identifier. In some embodiments, the summarizedinformation regarding a flow may also include information regarding theapplication associated with the flow. For example, a networking devicemay use the Network Based Application Recognition (NBAR) mechanism byCisco Systems, Inc., or a similar technology, to associate anapplication with a particular flow. Such a mechanism may use deep packetinspection (DPI) and/or analyze the addresses and ports of the flow, todetermine the type of data in the flow (e.g., webpage data, email data,etc.).

Referring now to FIGS. 4A-4B, an example is shown of network devicesreporting their flow monitoring capabilities to a DLA, according tovarious embodiments. As shown in FIG. 4A, assume that devices 22-40belong to a local network 400 that is connected to network backbone 130via a CE router 110, CE-N, which may also act as a DLA for local network400. For inter-network traffic flowing into or out of local network 400(e.g., via the Internet, VPN link, etc.), CE-N/DLA 110 may be able todirectly observe and analyze this traffic, since all such trafficdirectly traverses CE-N/DLA 110. In other implementations in whichmultiple CE routers are used at a given location, configuring two ormore of these routers as DLAs would have a similar effect, e.g., theDLAs will be able to observe all inter-network/site traffic from whicheither the source or destination of the traffic resides outside of thelocal network/site (different stores, different branch offices, etc.).

While a CE router acting as a DLA may be able to directly observe allinter-network traffic, other traffic flows may not be directlyobservable by the device. Notably, certain intra-site/network trafficmay not flow through the CE/DLA. For example, as shown, assume thatdevice 28 sends an intra-site traffic flow 402 to device 38 via devices30 and 36. Similarly, assume that device 22 sends intra-site trafficflow 404 to device 24 via device 32. In both cases, traffic flows402-404 may be outside of the direct observation of CE-N/DLA 110.

To perform anomaly detection on flows outside of the direct observationof a DLA, the devices through which these flows traverse may provideinformation regarding the flows back to the DLA. For example, as notedabove, a given networking device may be configured to generate asummarized traffic flow record for an observed traffic flow. However,not every device in the network may have this capability. For example,as illustrated in the example of FIG. 4A, assume that devices 34 and 36are flow-tracking capable (FTC) devices (e.g., by using NetFlow, NBAR,or similar technologies). Thus, device 36 may track and analyze theflows that traverse device 36, such as traffic flow 402. However, otherdevices in local network 400 may be basic networking devices, denoted“B” in FIG. 4A. Such devices may not have the same mechanisms toidentify and generate summarized records for their traffic flows.

In one aspect of the techniques herein, a discovery mechanism isintroduced that enables a DLA to discover the capabilities of eachnetworking device in a local network/site. For example, as shown in FIG.4B, devices 30-36 may provide capability data 406 to CE-N/DLA 110. Ingeneral, capability data 406 is indicative of whether or not a givendevice is operable to identify and analyze traffic flows. Althoughdescribed primarily with respect to a routed topology (e.g., Layer 3),similar techniques may be applied to switched environments (e.g., Layer2), using, for example, spanning trees or a protocol such as TRILL(e.g., by using a Layer 3 routing protocol for Layer 2 networks, whichalso supports VLAN).

Capability data 406 may be provided to CE-N/DLA 110 on either a pullbasis (e.g., in response to a request for the data from CE-N/DLA 110) oron a push basis (e.g., without first receiving a request for the data).In some embodiments, capability data 406 may be included in a customtype-length-value (TLV) in charge of encoding the capability of eachdevice in the L2/L3 domain (e.g., as either a DLA, FTC, or basicdevice). In some cases, capability data 406 may also include additionalinformation such as identifiers for the flow tracking mechanism(s) usedby a given device. For example, the capability data 406 may includeinformation regarding the type and/or version of the flow trackingmechanism used by the device (e.g., Netflow v5, Netflow v9, etc.),support for application recognition (e.g., NBAR, etc.), or the like. Insome embodiments, capability data 406 may be included in one or morecustom TLVs encapsulated in a routing protocol message such as an OSPFOpaque TLV of type 10 (or 11 if spanning across OSPF areas is required).In another embodiment, capability data 406 may be encapsulated in anIS-IS label switched path (LSP) message. This allows the DLA to retrievethe full topology of the site/local network (e.g., in the form of a linkstate database), augmented with the information regarding which devicesin the network can host additional DLAs, which devices can track andreport on traffic flows, and/or which devices are basic devices unableto distinguish traffic flows.

Referring now to FIGS. 5A-5B, examples of network devices providingtraffic flow data to a DLA are shown, according to various embodiments.In some cases, instructing all network devices to provide traffic flowdata to the DLA may be impractical due to constraints on the resourcesof the network. Accordingly, the DLA may determine which traffic flowsshould be reported to the DLA, thereby allowing the DLA to performanomaly detection on all relevant traffic within the local site/network.For purposes of illustration, let ‘T’ represent the L2 or L3 networktopology retrieved by the DLA where {FTC₁, . . . ,FTC_(i)} is the set offlow tracking capable devices and {B₁, . . . ,B_(j)} is the set of basicdevices that are not capable of flow tracking. In some embodiments, thebasic devices in {B₁, . . . ,B_(j)} may still be of use to the DLA forpurposes of anomaly detection by forwarding sampled copies of trafficflow packets to the DLA for further analysis, as detailed below. Invarious embodiments, the DLA may attempt to determine the minimum subsetof all devices carrying intra-site traffic for which flow-relatedinformation is required by the DLA, to have full coverage of allintra-site traffic for purposes of anomaly detection.

As shown in FIG. 5A, CE-N/DLA 110 may send a request 502 for trafficflow data to each of the devices in local network 400. Request 502 maybe a custom unicast message sent to each device individually or,alternatively, a multicast message sent to a multicast group used forSLN-based communications. In some cases, request 502 may be of the formflow_req(attributes) where the attributes parameter(s) specify a periodof time over which a device is to track and/or report flow data, a setof criteria for the traffic of interest (e.g., traffic that matches thespecified attributes, such as traffic associated with a particularapplication), whether the flow data should be reported with lowpriority, whether the flow data should be reported using output shaping,a specific reporting schedule for each device (e.g., to avoid a sharpincrease in traffic sent to the DLA, especially when packet copies aresent to the DLA by basic devices), combinations thereof, or the like.

As shown in FIG. 5B, in response to receiving a request 502 from theDLA, FTC devices 34 and 36 may identify their respective traffic flows,summarize the flows in traffic flow records 504, and provide thesummarized flow traffic flow records 504 back to the DLA. Such a recordmay include flow identifier information (e.g., the source anddestination addresses/ports of the flows, etc.), application typeinformation (e.g., HTTP traffic, email traffic, etc.), and/or anyobserved or calculated metrics regarding the flows (e.g., flow durationmetrics, flow size metrics, etc.). For example, a flow record 504provided by FTC device 34 to CE-N/DLA 110 may identify traffic flow 402as being sent by device 28 to device 38, the duration of flow 402 orsimilar metrics, the application type associated with traffic flow 402,etc.

In contrast to FTC devices, basic devices are not operable to identifyand summarize their respective traffic flows. However, according tovarious embodiments, the basic devices may still provide traffic flowdata back to the DLA in the form of copied packets, in response toreceiving a request from the DLA for traffic flow data (e.g., request502). For example, as shown, traffic flow data 506 sent by basic device32 to CE-N/DLA 110 may include copies of raw packets from traffic flow404. In other words, request 502 sent to basic device 32 may causedevice 32 to enable a packet copying mechanism and begin forwarding rawpackets from its traffic flows to CE-N/DLA 110 for a period of timespecified in request 502. In other words, CE-N/DLA 110 may base requests502 on the capabilities of the various devices, as indicated incapability data 406 (e.g., to request traffic flow records from FTCdevices, to enable packet copying at basic devices, etc.).

In various embodiments, packet copying mechanisms used by basic devicesmay allow for the analysis of traffic passing through switch ports bysending a copy of that traffic to another port on the switch to whichthe DLA is connected. For example, the basic devices may use theSwitched Port Analyzer (SPAN) mechanism of Cisco Systems, Inc. or asimilar mechanism, to provide raw packet copies to the DLA. Such apacket copying mechanism may also be configured to allow remote controlover the device, to enable remote monitoring of multiple switches acrossthe network (e.g., using the Remote SPAN mechanism of Cisco Systems,Inc., etc.). The traffic for each remote session may be carried over auser-specified virtual LAN (VLAN) through a reflector port and thenforwarded over trunk ports by participating intermediate switches to thedestination switch, which then mirrors the packets through thedestination port (e.g., to the DLA). Thus, to enable the forwarding oftraffic flow packets back to the DLA, request 502 sent from the DLA mayalso indicate a packet-copying session identifier, the source port(s)from which the packets are to be copied, traffic flow direction(s)(e.g., receive, transmit, or both), the reflector port, the destinationVLAN for the copied packets, the destination port for the packet copies,and/or any other request attributes (e.g., a sampling schedule, etc.) tothe source, intermediate, and destination switches.

When no request attributes are specified in request 502, the packetcopying mechanism of a basic device may be defaulted to remain activeuntil specified otherwise for all ports, excluding any destination orreflector ports. In another embodiment, request 502 may includeattributes that specify which types of traffic should be duplicatedbased on a match to specified criteria in request 502. For example,traffic flow data 506 from a basic device may include raw packets copiedbased on an access list, class-map, traffic flow direction, etc.specified in request 502. In another embodiment, only a subset of thepackets of a traffic flow may be copied, based on a parameter in request502. For example, a basic device may only send copies of every nthnumber of packets as part of traffic flow data 506 using a decreasedsampling frequency, to reduce processing overhead incurred by the packetduplication. In yet a further embodiment, the raw packets in trafficdata 506 from a basic device may be provided according to areporting/sampling schedule indicated in request 502, using a lowtraffic priority specified in request 502, and/or by employing trafficshaping on the provided traffic data 506, as requested in request 502.

Referring now to FIGS. 6A-6E, examples of a flow reporting strategybeing configured in a network is shown, according to variousembodiments. Once the DLA receives the summarized traffic flow recordsfrom the FTC devices and/or the raw packets from the basic devices, theDLA may use this information to devise a flow reporting strategy thatwould allow the DLA to perform anomaly detection on the intra-sitetraffic flows. In some embodiments, the DLA may standardize the receivedtraffic flow data by performing flow tracking on the raw packetsreceived from the basic devices. For example, the DLA may analyze theraw packets in traffic flow data 506 to generate and store summarizedtraffic flow records for the traffic flow data 506.

As shown in FIG. 6A, CE-N/DLA 110 may begin formulating the flowreporting strategy by first culling any inter-site traffic flows in itsstored flow records from further consideration. Notably, since CE-N/DLA110 also provides the interface between the local site/network 400 andnetwork backbone 130 (e.g., the Internet, MPLS VPN, etc.), CE-N/DLA 110may be able to directly observe and analyze any such inter-site trafficflows.

As shown in FIG. 6B, CE-N/DLA 110 may then determine a set of reportingdevices S={D₁, D₂, . . . ,D_(k)} that should report traffic flow data toCE-N/DLA 110 for purposes of inter-site anomaly detection. Suchreporting devices may include FTC devices that would report summarizedtraffic flow records to CE-N/DLA 110 and/or any basic devices that wouldprovide raw copies of traffic flow packets to CE-N/DLA 110, according tothe flow reporting strategy. In some embodiments, CE-N/DLA 110 mayselect the set S of reporting devices such that a minimum number ofdevices is selected, while still providing traffic flow data regardingall intra-site traffic to CE-N/DLA 110. For example, assume that alltraffic flows that traverse basic device 30 also traverse FTC device 36.In such a case, CE-N/DLA 110 may only include FTC device 36 in the set Sof reporting devices.

In some cases, there may be no FTC devices within the local site/networkor within a particular portion of the local network. This may result ina potentially high rate of traffic being sent to the DLA, due to thecopied packets. To reduce the processing load on the DLA device, the DLAmay determine that one or more other devices in the local network shouldanalyze raw packets from the basic devices. In one embodiment, the DLAmay cause a device in a branch to spin up a virtual machine dedicated totracking and summarizing traffic flows. In another embodiment, if thereare multiple routers in the site, one of the routers may be given theresponsibility of being a dedicated FTC device to receive and summarizeall copied traffic flow packets.

As shown in FIG. 6C, CE-N/DLA 110 may determine when each device in theset of reporting device should report traffic flow data to CE-N/DLA 110for purposes of anomaly detection. In one embodiment, CE-N/DLA 110 maydetermine a frequency at which each reporting device should sendsummarized traffic flow records or copied traffic flow packets toCE-N/DLA 110 for inspection. In another embodiment, CE-N/DLA 110 maydetermine reporting schedules for the reporting devices that avoidnetwork congestion, based on the predicted amount of reporting traffic,the topology T, and the observed link utilizations in local network 400.

As shown in FIG. 6D, CE-N/DLA 110 may determine a set of reporting pathsto be used by the reporting devices when sending the summarized trafficflow reports or copied flow packets to CE-N/DLA 110. Indeed, althoughvarious techniques may be used to reduce the overhead of sending thetraffic flow data to CE-N/DLA 110 for anomaly detection (e.g., byadjusting the timing of the reporting, etc.), network congestion maystill occur in LANs that include high speed links. In some embodiments,the DLA may keep track of historical observations of the traffic flowsin the local site/network and compute an estimate of the trafficoverhead due to these flows in light of the link utilization in thelocal network. In one embodiment, CE-N/DLA 110 may do so by requestingcongestion-related information in request 502. For example, CE-N/DLA 110may request information regarding the interface-based counters of thevarious devices, to report the level of utilization of a device's portsused by the L2/L3 routing protocols to provide the traffic flow data toCE-N/DLA 110. Alternatively, CE-N/DLA 110 may receive such informationfrom an NMS (e.g., one of servers 152-154, etc.) or from the routingprotocol itself. Notably, some routing protocols support protocolextensions to advertise routing adjacencies, address and linkutilization levels, etc.

Once the DLA determines an estimate of the amount of traffic that willresult from the reported traffic flow data, the DLA may determinewhether the estimated traffic overhead added to the regular trafficwould trigger potential congestion along the links in the local network.If so, the DLA may compute an alternate path (e.g., a traffic engineeredpath), to be used by a device when reporting traffic flow data to theDLA. In other words, an FTC or basic device selected by the DLA as areporting device may send the reported traffic flow data to the DLAalong a different path than the one computed by the L2/L3 routingprotocol. If an alternate path is computed, the request message (e.g.,request 502) and/or any flow reporting instructions sent as part of theflow reporting strategy may be further augmented to specify thereporting path.

As shown in FIG. 6E, once CE-N/DLA 110 determines the traffic flowreporting strategy to be used in local network 400, it may specify thecorresponding set of actions to be taken by the devices, so that trafficflow data may be provided to the DLA for purposes of performing anomalydetection on the intra-site traffic. For example, CE-N/DLA 110 may senda traffic flow reporting instruction 602 to the devices in local network400 that were selected as reporting devices. Traffic flow reportinginstruction 602 sent to a reporting device may, for example, specify areporting schedule (e.g., a reporting frequency, a scheduled reportingtime, etc.), a reporting path (e.g., an alternate path via which thereported traffic flow data is to be sent), an aggregation device (e.g.,to consolidate copied flow packets into traffic flow records at a deviceother than the DLA), parameters that control how the reported trafficflow data is to be sent (e.g., a priority, traffic engineeringparameters, etc.), parameters that control which types of traffic flowsare to be reported to the DLA (e.g., based on application type, ports oraddresses of the source/destination, etc.), or any other parameters thatcontrol how and when a reporting device provides traffic flow data tothe DLA (e.g., either summarized flow records or raw packet copies).

In various embodiments, the techniques herein may be repeated any numberof times to dynamically adjust the traffic flow reporting strategy usedin the network for purposes of anomaly detection. For example, the DLAmay request flow data from all devices in the local site ever X hours,days, etc., to discover new flows faster (e.g., where X is lower thanthe reporting frequency used by the reporting strategy). Alternately, orin addition thereto, a given network device (e.g., an FTC device, etc.)may send traffic flow data to the DLA in an unsolicited manner, if thedevice determines that a new flow has appeared.

FIG. 7 illustrates an example simplified procedure for determining atraffic flow reporting strategy in a network, in accordance with variousembodiments herein. Procedure 700 may be performed, for example, by aDLA or other device configured to control the traffic flow reportingstrategy used in a network for purposes of anomaly detection. Procedure700 may begin at step 705 and continue on to step 710 where, asdescribed in greater detail above, a first device receives traffic flowdata from a plurality of devices in the network. The traffic flow datamay be of different forms, depending on the capabilities of the devices.For example, the plurality of devices may include devices that areconfigured to identify and summarize traffic flows and include thesummarized traffic flow records in the received traffic flow data. Inother embodiments, one or more of the plurality of devices may be abasic device that is not operable to track traffic flows. Such a basicdevice may instead provide copies of traffic flow packets to the firstdevice for further analysis.

At step 715, the first device selects a set of reporting devices fromamong the plurality of devices based on the received traffic flow data,as described in greater detail above. In some embodiments, the firstdevice may select the set of reporting devices to include a minimalnumber of devices from the plurality, while still providing sufficientinsight into the traffic flows within the local network. For example,the first device may exclude a particular device from the set ofreporting devices if the traffic flow data from the particular device isredundant with traffic flow data from a different device. For basicdevices that report only raw packets to the first device, the firstdevice may, in one embodiment, convert the raw packets into summarizedtraffic flow records, to identify candidate reporting devices (e.g.,based on the corresponding flows). In further embodiments, the firstdevice may select the set of reporting devices by first excluding anyinter-site/network traffic flows from consideration, so that onlyintra-site/network traffic flows are reported to the first device.

At step 720, as detailed above, the first device provides traffic flowreporting instructions to the selected set of reporting devices. Ingeneral, the first device may generate a traffic flow reportinginstruction such that the impact of the flow reporting on the network isminimized. In one embodiment, the instruction may include flow reportingschedules/frequencies determined by the first device to reduce networkcongestion. In another embodiment, the instruction may specify areporting path to be used by a particular reporting device. For example,the first device may analyze reported link information from the networkdevices (e.g., a history of flow reports), routing information from therouting protocol, information from an NMS, or the like, to identifyalternative routing paths that may alleviate network contention due tothe flow reporting. In further embodiments, a flow reporting instructionsent to a particular reporting device may cause the reporting device tosend raw packets to an FTC device, so that the corresponding flows canbe identified from the packets. Thus, the traffic flow reportinginstructions may cause the reporting devices to implement a flowreporting strategy whereby the reporting devices send sampled trafficflow data to an anomaly detection device for purposes of anomalydetection.

In some embodiments, the first device may be the anomaly detectiondevice and may detect an anomaly in the network using the sampledtraffic data received from the reporting devices, as described ingreater detail above. Notably, the sampled traffic data may include allpackets or traffic flow records over a given time period or only asubset of the available data (e.g., every nth packet, etc.). The firstdevice may detect the anomaly, for example, by comparing the trafficflow data to a learning machine model (e.g., a clustering model, etc.),an analytic model (e.g., a mathematical model), or any other modelconfigured to determine whether the reported traffic flow data isindicative of an anomaly in the network. Procedure 700 then ends at step725.

FIG. 8 illustrates an example simplified procedure for receiving atraffic flow reporting instruction, in accordance with one or moreembodiments described herein. For example, procedure 800 may beperformed by a network device in communication with a DLA or otherdevice configured to determine a traffic flow reporting strategy forpurposes of anomaly detection. Procedure 800 may begin at step 805 andcontinue on to step 810 where, as described in greater detail above, afirst device in a network may provide capability data to a second device(e.g., an anomaly detection device, etc.). The capability data may, forexample, identify whether or not the first device is able to providesummarized traffic flow records to the anomaly detection device. If thefirst device is capable of providing traffic flow records, the firstdevice may indicate so as part of the capability data and may includeadditional information such as the type or version of its traffic flowtracking mechanism (e.g., whether the device is able to distinguishbetween different applications, etc.) in the capability data.Conversely, if the first device is a basic device that is not operableto track flows, it may indicate in capability data that can insteadprovide raw traffic flow packet copies. In such a case, the capabilitydata may include any additional information regarding the packet copyingmechanism used by the device (e.g., the ports used, etc.). In variousembodiments, the first device may provide the capability data to thesecond device either on a push basis or on a pull basis.

At step 815, as detailed above, the first device receives a request fortraffic flow data from the second device. Such a request may indicatethe types of traffic flow data to report (e.g., based on applicationtype, based on ports or addresses, etc.). The request may also indicatethe times during which the first device is to generate the traffic flowdata and/or provide the traffic flow data back to the second device, apriority to be assigned to the reported traffic flow data, trafficshaping parameters for the reported flow data, or the like.

At step 820, as described in greater detail above, the first deviceprovides the requested traffic flow data to the second device. Theprovided traffic flow data may include summarized traffic flow recordsor, alternatively, include raw traffic flow packets copied by the firstdevice. In one embodiment, the traffic flow data may also includeadditional information such as the ports or addresses used by the firstdevice for purposes of forwarding traffic (e.g., to allow the anomalydetection device to identify alternative reporting paths). In responseto receiving the provided traffic flow data, the second device may usethe traffic flow data to determine a traffic flow reporting strategy andgenerate any corresponding traffic flow reporting instructions for theselected reporting devices in the network.

At step 825, the first device may receive a traffic flow reportinginstruction that causes the first device to provide sampled traffic flowdata to an anomaly detection device in the network. Such sampled datamay include either raw packets that are copied from the sampled trafficflow or summarized traffic flow records, depending on the capabilitiesof the first device. In one embodiment, the instruction may specify areporting schedule (e.g., an indication as to when the first device isto send the sampled traffic flow data to the anomaly detection device).In other embodiments, the instruction may specify a reporting path thatthe first device is to use when reporting the sampled traffic flow datato the anomaly detection device (e.g., a path that differs from therouting path selected by the routing protocol in use). In furtherembodiments, the instruction may cause the first device to provide rawtraffic flow packets to the anomaly detection device via another networkdevice configured to generate traffic flow records from the packets.Using the sampled traffic flow data, the destination anomaly detectiondevice may determine whether the corresponding traffic flows areanomalous, such as during a network attack. Procedure 800 then ends atstep 830.

It should be noted that while certain steps within procedures 700-800may be optional as described above, the steps shown in FIGS. 7-8 aremerely examples for illustration, and certain other steps may beincluded or excluded as desired. Further, while a particular order ofthe steps is shown, this ordering is merely illustrative, and anysuitable arrangement of the steps may be utilized without departing fromthe scope of the embodiments herein. Moreover, while procedures 700-800are described separately, certain steps from each procedure may beincorporated into each other procedure, and the procedures are not meantto be mutually exclusive.

The techniques described herein, therefore, allow a DLA of a SLN todetect anomalies (e.g., DDoS attacks, etc.) in traffic that does nottransit through the DLA device, thereby expanding the scope of theanomaly detection to include intra-site traffic. The techniques hereinalso support networks with devices of varying capabilities, such devicesthat support traffic flow monitoring/tracking, basic devices that do notsupport traffic flow tracking but use a packet copying mechanism, etc.

While there have been shown and described illustrative embodiments thatprovide for information reporting for anomaly detection, it is to beunderstood that various other adaptations and modifications may be madewithin the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein whereby the DLAdetermines the flow reporting strategy to be used in the local network,any other device may also determine the strategy, in other embodiments.In addition, while certain protocols are shown, such as BGP, othersuitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

1-13. (canceled)
 14. A method, comprising: providing, by a first devicein a network, capability data to a second device in the network, whereinthe capability data is indicative of whether the first device isoperable to generate summarized traffic flow records; receiving, at thefirst device, a request for traffic flow data from the second devicebased on the capability data; providing, by the first device, therequested traffic flow data to the second detection device, in responseto receiving the request for the traffic flow data; and receiving, atthe first device, a traffic flow reporting instruction from the seconddevice based on the traffic flow data provided to the second device,wherein the traffic flow reporting instruction causes the first deviceto report sampled traffic flow data to an anomaly detection device. 15.The method as in claim 14, wherein the capability data indicates thatthe first device is not operable to generate summarized traffic flowrecords, and wherein the sampled traffic flow data provided to theanomaly detection device comprises raw traffic flow packets copied bythe first device.
 16. The method as in claim 14, wherein the trafficflow reporting instruction indicates a reporting schedule to be used bythe first device when reporting the sampled traffic flow data.
 17. Themethod as in claim 14, wherein the traffic flow reporting instructionindicates a reporting path to be used by the first device when reportingthe sampled traffic flow data, and wherein the reporting path differsfrom a routing path computed by a routing protocol between the firstdevice and the anomaly detection device.
 18. The method as in claim 14,wherein the traffic flow reporting instruction causes the first deviceto report the sampled traffic flow data to the anomaly detection devicevia another network device configured to aggregate the sampled trafficflow data into a summarized traffic flow report. 19-23. (canceled) 24.An apparatus, comprising: one or more network interfaces to communicatewith a network; a processor coupled to the one or more networkinterfaces and configured to execute a process; and a memory configuredto store the process executable by the processor, the process whenexecuted operable to: provide capability data to a device in thenetwork, wherein the capability data is indicative of whether theapparatus is operable to generate summarized traffic flow records;receive a request for traffic flow data from the device based on thecapability data; provide the requested traffic flow data to the device,in response to receiving the request for the traffic flow data; andreceive a traffic flow reporting instruction from the device based onthe traffic flow data provided to the device, wherein the traffic flowreporting instruction causes the apparatus to report sampled trafficflow data to an anomaly detection device.
 25. The apparatus as in claim24, wherein the capability data indicates that the first device is notoperable to generate summarized traffic flow records, and wherein thesampled traffic flow data provided to the anomaly detection devicecomprises raw traffic flow packets copied by the apparatus.
 26. Theapparatus as in claim 24, wherein the traffic flow reporting instructionindicates a reporting schedule or a reporting path to be used by theapparatus when reporting the sampled traffic flow data.
 27. Theapparatus as in claim 24, wherein the traffic flow reporting instructioncauses the apparatus to report the sampled traffic flow data to theanomaly detection device via another network device configured toaggregate the sampled traffic flow data into a summarized traffic flowreport.